{"id":"W4210336058","doi":"10.1186/s12874-021-01493-6","title":"Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management","year":2022,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; Public Health Ontario; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; Government of Ontario","keywords":"Visualization; Data science; Computer science; Traumatic brain injury; Psychology; Medicine; Data mining; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004398342,0.0006366349,0.0004956061,0.006358152,0.0005168131,0.003095825,0.000882648,0.0003595086,0.001689249],"category_scores_gemma":[0.01884282,0.0002210597,0.0009476836,0.004377423,0.0006965944,0.001434473,0.001336235,0.000599507,0.0001748643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142698,"about_ca_system_score_gemma":0.002172727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008121681,"about_ca_topic_score_gemma":0.01191996,"domain_scores_codex":[0.9983634,0.0009709182,0.00013967,0.000174641,0.0002930714,0.00005822051],"domain_scores_gemma":[0.9887918,0.007216349,0.00156716,0.001071267,0.001040156,0.0003134095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002687539,0.0002568854,0.5747179,0.0007101026,0.0007545678,0.0003170647,0.003387921,0.03450858,0.002841451,0.01838724,0.005605488,0.358244],"study_design_scores_gemma":[0.00006749745,0.0003716421,0.4201885,0.0007789779,0.000382345,0.0007961905,0.01060922,0.4282118,0.006588451,0.106784,0.02502465,0.0001968332],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5662743,0.004017054,0.4013079,0.01112506,0.0002172765,0.0006205643,0.006341635,0.002536943,0.007559282],"genre_scores_gemma":[0.7786797,0.001232011,0.2180391,0.0001500538,0.00006885835,0.0001752632,0.001291612,0.0000812415,0.0002822006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008121681,"threshold_uncertainty_score":0.02326095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6092069429490466,"score_gpt":0.6093537206932703,"score_spread":0.0001467777442236695,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}